Mobile wireless communications devices, tablets, and similar devices have touch screens that often are equipped with proximity detectors, such as infrared sensors, that detect simple gestures. For example, the devices may detect the approach or movement of an object, such as a finger or mechanical stylus. This detection may be used to disable a touch screen function for the mobile wireless communications device during a call when the device is near the ear of a user. Infrared sensors may use the brightness reflected by the target object to determine a rough estimate of the distance to the moving object.
Other more complicated gesture recognition systems interpret simple hand gestures to enable touchless gesture control of wireless communications devices, tablets and similar devices. The device may respond to simple, touchless commands, distinguishing between more complicated simple hand gestures. These systems allow intuitive ways for users to interact with their electronic devices. For example, a hand gesture, such as a hand wipe, may instruct the device to implement a page turn for a book application on a tablet. These current hand gesture recognition systems, however, involve intensive processing of data using complicated algorithms, often including time-of-flight and machine learning based algorithms that require extensive computations to discriminate between even the most common hand gestures, such as a single tap or single wipe. More efficient hand gesture recognition systems are desired to facilitate their use not only with smaller and more compact electronic devices, such as cell phones and tablets but also consumer electronic devices, such as light dimmers and water faucets, without using excessive processing resources and memory.